Hello, can one use predict, as you can with other model objects like lm,
with dynlm to predict a new data set that is identical in field names,
just a different time period. 

Be nice if you could, I don't really want to create a new data set with
all the lags, hoping it would generate dynamically.  Does not seem to
work, get a # of column error.  Any suggestions?


R> str(dfz)
An 'xts' object from 2009-09-25 09:45:06 to 2009-10-19 15:00:57 containing:
  Data: num [1:28232, 1:8] 0.54771 -0.00825 1.27406 0.69705 1.08107 ...
 - attr(*, "dimnames")=List of 2
  ..$ : NULL
  ..$ : chr [1:8] "PC1" "PC2" "PC3" "PC4" ...
  Indexed by objects of class: [POSIXt,POSIXct] TZ: GMT
  xts Attributes:  
 NULL

R> str(z)
An 'xts' object from 2009-10-21 09:45:04 to 2009-10-21 15:00:56 containing:
  Data: num [1:2304, 1:8] -0.5044 1.237 -0.7764 0.3931 0.0629 ...
 - attr(*, "dimnames")=List of 2
  ..$ : NULL
  ..$ : chr [1:8] "PC1" "PC2" "PC3" "PC4" ...
  Indexed by objects of class: [POSIXt,POSIXct] TZ: GMT
  xts Attributes:  
 NULL


dols = dynlm(FAS0 ~ L(FAS0,1:10) + L(PC1,0:10) + L(PC2,0:10) +
L(PC3,0:10) + L(PC4,0:10) + L(PC5,0:10) + L(PC6,0:10) + L(PC7,0:10),
data=dfz)

R> predict(dols,newdata=z)

/*Error in fix.by(by.x, x) : 'by' must match numbers of columns*/

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